This invention relates to a method for encoding a compressed speech signal obtained by dividing an input audio signal such as a speech or sound signal into blocks, converting the blocks into data on the frequency axis, and compressing the data to provide a compressed speech signal, and to a method for decoding a compressed speech signal encoded by the speech encoding method.
A variety of compression methods are known for effecting signal compression using the statistical properties of audio signals, including both speech and sound signals, in the time domain and in the frequency domain, and taking account of the characteristics of the human sense of hearing. These compression methods are roughly divided into compression in the time domain, compression in the frequency domain, and analysis-synthesis compression.
In compression methods for speech signals, such as multi-band excitation compression (MBE), single band excitation compression (SBE), harmonic compression, sub-band coding (SBC), linear predictive coding (LPC), discrete cosine transform (DCT), modified DCT (MDCT) or fast Fourier transform (FFT), it has been customary to use scalar quantizing for quantizing the various parameters, such as the spectral amplitude or parameters thereof, such as LSP parameters, .alpha. parameters or k parameters.
However, in scalar quantizing, the number of bits allocated for quantizing each harmonic must be reduced if the bit rate is to be lowered to, e.g., approximately 3 to 4 kbps for further improving the compression efficiency. As a result, quantizing noise is increased, making scalar quantizing difficult to implement.
Thus, vector quantizing has been proposed, in which data are grouped into a vector expressed by one code, instead of separately quantizing data on the time axis, data on the frequency axis, or filter coefficient data which are produced as a result of the above-mentioned compression.
However, the size of the codebook of a vector quantizer, and the number of operations required for codebook searching, normally increase in proportion to 2b, where b is the number of bits in the output (i.e., the codebook index) generated by the vector quantizing. Quantizing noise is increased if the number of bits b is too small. Therefore, it is desirable to reduce the codebook size and the number of operations for codebook searching while maintaining the number of bits b at a high level. In addition, since direct vector quantizing of the data resulting from converting the signal into data on the frequency axis does not allow the coding efficiency to be increased sufficiently, a technique is needed for further increasing the compression ratio.
Thus, in Japanese Patent Application Serial No. 4-91422, the present Assignee has proposed a high efficiency compression method for reducing the codebook size of the vector quantizer and the number of operations required for codebook searching without lowering the number of output bits of the vector quantizing, and for improving the compression ratio of the vector quantizing. In this high efficiency compression method, a structured codebook is used, and the data of an M-dimensional vector is divided into plural groups to find a central value for each of the groups to reduce the vector from M dimensions to S dimensions (S&lt;M). First vector quantizing of the S-dimensional vector data is performed, an S-dimensional code vector is found, which serves as the local expansion output of the first vector quantizing. The S-dimensional code vector is expanded to a vector of the original M dimensions, and data indicating the relation between the S-dimensional vector expanded to M dimensions and the original M-dimensional vector, and second vector quantizing of the data is performed. This reduces the number of operations required for codebook searching, and requires a smaller memory capacity.
In the above-described high efficiency compression method, error correction is applied to the relatively significant upper-layer codebook index indicating the S-dimensional code vector that provides the local expansion output in the first quantizing. However, no practical method for performing this error correction has been disclosed.
For example, it is conceivable to implement error correction in a compressed signal transmission system in which the encoder is provided with a measure for detecting errors for each compression unit or frame, and is further provided with a convolution encoder as a measure for error correction of the frame, and the decoder detects errors for each frame after implementing error correction utilizing the convolution encoder, and replaces the frame having an error by a preceding frame or mutes the resulting speech signal. However, even if one bit of bits subject to error detection has an error after the error correction, the entire frame containing the erroneous bit is discarded. Therefore, when there are consecutive errors, a discontinuity in the speech signal results, causing a deterioration in perceived quality.